Global Navigation Satellite Systems (GNSS), such as GPS, are often used for geo-localization and navigation. These GNSS systems (e.g., Galileo, GLONASS, Beidou) consist of constellations of satellites in space, with each satellite wirelessly broadcasting signals containing information so that corresponding earth-based receivers can determine their geo-location. The use of GNSS has become ubiquitous with the advent of consumer mobile electronic devices. Several satellite systems have been deployed in recent years, resulting in multiple GNSS satellite constellations. As more GNSS receivers become capable of interacting with each of these satellite systems, the application of the systems and methods proposed herein will only be enhanced. However, GNSS localization quality is often degraded. This degradation is especially prevalent in urban areas, including signal blockage and multi-path reflections from buildings, trees, terrain, and other obstacles.
The disclosed techniques demonstrate that simultaneous localization and mapping (SLAM) can be performed using only GNSS SNR and geo-location data, collectively termed GNSS data henceforth. A principled Bayesian approach for doing so is disclosed, but alternative approaches at varying degrees of complexity and accuracy may be employed by one skilled in the art.
In GNSS and other wireless communication, line-of-sight (LOS) channels are characterized by statistically higher received power levels than those in which the LOS signal component is blocked (e.g., non-LOS or NLOS channels). As a mobile GNSS receiver traverses an area, obstacles (e.g., buildings, trees, terrain) frequently block the LOS component of different satellite signals, resulting in NLOS channels characterized by statistically lower signal-to-noise ratios (SNR). Many GNSS receivers can also log the transmitted or computed geo-location data, including per-satellite azimuth/elevation, SNR, and the latitude/longitude of the receiver.
In addition to GNSS localization, three-dimensional (e.g., 3-D) maps of urban environments are of tremendous importance for several types of commercial applications. However, existing methods for 3-D mapping of city environments are expensive or necessitate significant planning and surveying, such as using LiDAR surveying or aerial photography surveying.
Due to growing demand for accurate geo-location services and the fact that GNSS positioning accuracy degrades in cluttered urban environments, existing mapping solutions have focused on refining position estimates by using known 3-D environment maps. In some embodiments, “shadow matching” (SM) may improve localization. Essentially, SM involves detecting occluded signals and matching the corresponding points of reception to areas inside the “shadows” of the signal-blocking buildings, thereby constraining the space of possible receiver locations. SM may be implicitly performed by first learning and then matching against SNR measurement models, where the models may vary as a function of the receiver coordinates, and where the LOS/NLOS channel conditions are modeled probabilistically.
Using GNSS signal strength to construct 3-D environment maps is a relatively new area of research. Generally, GNSS-derived information is used to identify shadows of buildings with respect to different satellite configurations (as in SM), after which ray-tracing methods may be used to build environment maps. However, existing methods rely on a collection of heuristics resulting in “hard” maps, with no notion of map uncertainty and no straightforward way to update the map in a principled, consistent manner when additional measurements become available.
Due to the complexity of mapping problems, some existing solutions rely on probabilistic techniques to estimate maps. For applications focusing on the mapping of urban landscapes, some form of obstacle detection is typically used. This may use sensors, such as LiDAR mounted on the sensing vehicle, that take obstacle-range readings in chosen directions but with respect to a local reference frame. Without correction, such active mapping techniques can be plagued by self-localization errors (e.g., position/velocity errors, orientation errors), leading to a data association problem. As a result, some approaches employ a class of algorithms known as simultaneous localization and mapping (SLAM) to estimate the map and localize the sensing vehicle jointly. What is needed in the art is a localization approach that reduces the effects of localization errors.